A novel hybrid model for image classification Online publication date: Wed, 18-Mar-2015
by Yi-Ming Liu, Min Yao, Rong Zhu
International Journal of Computational Science and Engineering (IJCSE), Vol. 6, No. 1/2, 2011
Abstract: Recently, biological intelligent computing gains more and more attention in analysing large-scale real world datasets. Because the performance of the support vector machine (SVM) classifier is always degraded by poor feature subsets and inappropriate parameters for training, an improved quantum-behaved particle swarm optimisation (IQPSO) is introduced to optimise the features and parameters synchronically, aiming to improve the generalisation of the SVM classifier. That is, a novel hybrid image classification model by combing SVM and IQPSO, called as IQPSO_SVM is presented in this paper. Experimental results show that the proposed IQPSO_SVM improves the classification accuracy greatly compared to the traditional SVM with grid search, and outperforms such SVM based on genetic algorithm (GA_SVM) without accuracy loss.
Online publication date: Wed, 18-Mar-2015
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